Seismic Attribute Analysis technologies are having a significant impact on upstream geoscience activities in oil exploration. Visualization and analysis of seismic attribute volumes in a 3D earth model in an interactive setting can significantly improve geological object detection, including subtle feature identification. One of the tasks and challenges in these environments is to be able to effectively and efficiently analyze and visualize the data volume to aid the discovery and analysis of geological objects and their related rock and fluid properties.
The study of seismic attributes provides qualitative information of the geometry (such as lateral continuity, depositional pattern) and physical parameters (such as impedance, reflection coefficients) of the subsurface. A number of attributes are typically derived from a data set to represent or extract a certain physical property. For example, a set of attributes, such as amplitude maximum, minimum, interval amplitudes, and directions, are used to enhance the information that might be subtle in original seismic data. Others such as diffusion, spectral decomposition, and discontinuity data cube could be used for fault or horizon detection purposes.
Lees, J., et al. in EP 1696247, “System And Method for Analyzing and Imaging an Enhanced Three-Dimensional Volume Data Set Using One or More Attributes,” describe a process for creating a combination attribute volume or combo volume by combining one or more attribute volumes into a single volume. The resulting combined volume may then be displayed and a seed pick positioned on an event of interest such as a geological body. An auto-picker function will then find all the connecting points.
Andersen, J., et al. in their patent publication US 2010/0171740, “Visualizing Region Growing in Three Dimensional Voxel Volumes” disclose a process that may be summarized as generating a 3D scene having a plurality of voxels for representing a volume data set of seismic data collected from the oilfield, defining a segmentation algorithm for segmenting the volume data within the 3D scene, the segmentation algorithm comparing a pre-determined threshold to an attribute of a voxel for a plurality of voxels, defining a control parameter associated with the attribute for controlling the segmentation algorithm, adjusting the control parameter to guide the segmentation algorithm in segmenting the volume data set to generate a visualized geobody, and displaying the visualized geobody.
Andersen, J., et al. in their paper “Delineation of Geological Elements from RGB Color Blending Seismic Attributes Volumes” describe a widely used multi-attribute visualization technique based on color blending. In this technique, data samples are mapped based on a three dimensional color space, namely red, blue and green components, based on three corresponding seismic attributes.
In U.S. Pat. No. 5,838,634 to Jones et al. (“Method Of Generating 3-D Geologic Models Incorporating Geologic And Geophysical Constraints”), features of subsurface earth reservoirs of interest are made available for analysis and evaluation by forming three-dimensional, geologic block models based on field data. The field data include geological observations, such as lithofacies and porosity values obtained from well data and other sources, as well as geophysical data, usually from seismic surveys. The geologic models representative of subsurface reservoirs so obtained are optimized to match as closely as feasible geologic constraints known or derived from observed geologic data. The models also conform to geophysical based constraints indicated by seismic survey data. The modeled geologic lithofacies and porosity are converted into acoustic velocity and bulk density values, which are then formulated as a seismic response which is then compared with actual seismic data. A perturbation process on lithofacies and porosity can be iteratively repeated until a representation of the reservoir is obtained which is within specified limits of accuracy or acceptability.
Washbourne et al. in their patent application publication WO 2008/154640, “Optimizing Amplitude Inversion Utilizing Statistical Comparisons of Seismic to Well Control Data,” describe a method for obtaining enhanced seismic data and optimized inverted reflectivity, which includes computing statistical well characterizations based upon band-limited well reflectivity for a subsurface region. The seismic data are inverted using an optimal seismic inversion algorithm to produce a set of optimized inverted reflectivity.
Imhof, et al. in their patent application publication WO 2011/49609, “Method for Seismic Hydrocarbon System Analysis,” propose a method for analyzing seismic data representing a subsurface region for presence of a hydrocarbon system or a particular play. Seismic attributes are computed, the attributes being selected to relate to the classical elements of a hydrocarbon system, namely reservoir, seal, trap, source, maturation, and migration.
Pascal Klein et al. in their paper “3D Curvature Attributes: A New Approach for Seismic Interpretation,” disclose a method to compute volumetric curvatures and their application to structural closure and qualitative estimation of basic fracture parameters. Their method allows the quantification and qualification of lateral continuity of the fault and its vertical displacement.
Chopra and Marfurt in their paper “75th Anniversary Seismic attributes—A Historical Perspective” (Geophysics 70, pages 3SO-28SO (September-October 2005); describe the historical view of seismic attributes and their development. The creation, processing and visualization of seismic attributes have contributed to the reflector acquisition, mapping, fault identification, bright spot identification, among other things. Techniques such as clustering, self-organized maps, geostatistics, and neural nets have extended their capabilities.
R. Banchs and J. Jimenez in their paper “Content Addressable Memories for Seismic Attribute Pattern Recognition,” EAGE 64th Conference & Exhibition—Florence, Italy (May 27-30, 2002) describe a seismic attribute pattern recognition method based on content addressable memories for the purpose of reservoir characterization. The method allows the classification of seismic facies/class maps in which each of the classes is related to a predefined reference location.
In publications such as those described above, computational techniques such as discrimination analysis, geostatistics, supervised training, unsupervised classification and calibrations are used for analyzing or interpreting various seismic attributes. Furthermore, multiple volume blending and co-rendering of seismic attributes has also been used extensively in an interactive interpretation environment for visualizing and delineation of regions of interest. The term co-rendering means to display at least two data volumes for viewing, typically on a computer monitor or similar output display device.
The combination of computational methods and multi-dimensional transfer functions of opacity control and color mapping has been used to highlight, display and classify areas of interest, reveal channel structure, identify stratigraphic features as well as classify facies boundaries. However, due to the complexity and inter-dependence of multi-dimensional attributes, optimal rendering using current techniques requires extensive data knowledge and visualization experience to optimally manipulate parameters and calibrations to extract key geological insights from these data. For novice and even experienced users, these activities are time-consuming tasks and can result in errors and possibly lost opportunities due to sub optimal parameter selection.